Speech enhancement has been actively studied for decades [1]}, [2]}, [3]}, [4]}. In recent years, deep neural networks have greatly advanced speech enhancement using both supervised [5]}, [6]}, [7]} and unsupervised methods [8]}, [9]}, [10]}, [11]}, [12]}. Supervised methods have achieved overwhelming performance, but they require access to ground-truth signals, and thus they can degrade on real recordings which are mismatched with the simulated data used for training. Unsupervised methods overcome these problems by requiring only the noisy speech signals.
